Search Results for author:
Found 9 papers, 6 papers with code
GAUDI: A Neural Architect for Immersive 3D Scene Generation
We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera.
Ranked #1 on
Image Generation
on ARKitScenes
ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data
It is not only the first RGB-D dataset that is captured with a now widely available depth sensor, but to our best knowledge, it also is the largest indoor scene understanding data released.
ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds
Our shape-aware adversarial attacks are orthogonal to existing point cloud based attacks and shed light on the vulnerability of 3D deep neural networks.
Order-Aware Generative Modeling Using the 3D-Craft Dataset
We demonstrate that our Voxel-CNN model is simple and effective at this creative task, and can serve as a strong baseline for future research in this direction.
Why Build an Assistant in Minecraft?
In this document we describe a rationale for a research program aimed at building an open "assistant" in the game Minecraft, in order to make progress on the problems of natural language understanding and learning from dialogue.
CraftAssist: A Framework for Dialogue-enabled Interactive Agents
This paper describes an implementation of a bot assistant in Minecraft, and the tools and platform allowing players to interact with the bot and to record those interactions.
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero
The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy.
A Deep Visual Correspondence Embedding Model for Stereo Matching Costs
This paper presents a data-driven matching cost for stereo matching.
Large Displacement Optical Flow from Nearest Neighbor Fields
We use approximate nearest neighbor fields to compute an initial motion field and use a robust algorithm to compute a set of similarity transformations as the motion candidates for segmentation.
